The case study details how Trace3 used machine learning to help a healthcare organization protect patient data and reduce false positives in access monitoring. Using a five-phase approach—Listen, Educate, Prototype, Plan, and Buy-in—Trace3 analyzed workflows and implemented an ensemble ML solution combining behavioral, volumetric, and false-positive models. This system improved detection accuracy, reduced false positives from 90% to 30%, and enhanced scalability and response speed. Supported by an upgraded interface and training, the solution fostered a security culture that strengthened HIPAA compliance and reduced operational costs.

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